Understanding the new role of IP management within the digital transformation in industry and commerce

More than 120 participants discussed the new challenges for management in the context of the digitization in Strasbourg. The revolutionary changes are affecting all institutions within innovation systems, like industries, universities and public institutions. The conference had a true interdisciplinary set up with speakers and audience having economic, legal and technology background as well as scholars, practitioners and representatives from institutions like the European Patent Office, WIPO and the United States State Department.

CEIPI the Center for International Intellectual Property Studies, BETA the Bureau for Economic Theory and Applications and I3PM the International Institute for IP Management organized this Conference on Intellectual Property and Digitization at MISHA the Maison Interuniversitaire des Sciences de l’Homme in Strasbourg on 4th May 2017.

In their welcome address, Prof. Christoph Geiger, Prof. Julien Pénin, Xavier Seuba and Peter Bittner emphasized the interdisciplinary approach of the event. They stated that the digital transformation is not a new topic, but that it becomes more and more ubiquitous. As a consequence the dynamics of IP management issues is getting higher and the profession of IP managers becomes more important in companies which have to cope with the transformation as well in their value chain as in their products and services. It is also more and more becoming a fact that companies have to deal with new, digitized business models, digitized business processes and new smart digital products. This culminates in the statement: What can be digitized will be digitized.

First part – Dedicated to Digital Transformation and Risk Management

Chair: Julien Pénin, Professor in Economics, BETA, University of Strasbourg

Keynote No. 1: Fostering Innovation in Digital Trade

Keith Maskus – Chief Economist for United States State Department, Arts and Sciences Professor of Distinction and Professor of Economics at the University of Colorado Bolder.

Digital Transformation forces us to make regulatory tradeoffs. The recommendation is to improve the global interoperability for digital trade – reducing international trade barriers.

Europe is on its way to a single digital market which is a huge challenge. Traditional trade becomes more and more e-commerce and what we see is just the beginning; digital services have the potential to be transformative for all kinds of sectors. The Size of the digital economy is huge and growing. According to a 2016 study: The value of the EU data economy was estimated at EUR 257 Billion in 2014 (around 1.85% of EU GDP) up to EUR 272 Billion in 2015. For the improvement of cross-border digital trade, we need a supportive framework for example in copyright protection, a balance between creative participants and structure limitations and exceptions. For example: Internet provider liability rules, treatment of data privacy, risk management and anti-fraud enforcement have serious consequences for business models. Maskus states the necessity of global interoperability of digital markets, to lower international trade barriers in order to reducing transaction costs. Example copyrights and high transaction costs for fragmented licensing systems: We have to solve a dilemma and balance between permissive use for digital creativity and the need to permit content providers control. The digital environment makes this tradeoff both harder to devise and easier to enforce. Maskus presented a long research agenda for much better data on international digital trade and based on the need of a framework right for creativity and competition.

First Panel: The Digital Transformation – From Freedom to Operate to Risk Management

Sean O’Connor – Boeing International Professor, Director for Advanced Study & Research on Innovation Policy (CASRIP), University of Washington School of Law.

Sean O’Conner: Preemption, Abstract Ideas and Digitization. Business is more and more transforming from selling physical products to selling services. One of the key questions in this context is, how to construct and organize the selling process. Advocating continued flexibility of innovative business models while rejecting one-size-fits-all imposition of sale transactions. One challenge is the question, what we really buy and own in a digital economy. What we see is an ongoing transaction flexibility and heightened scrutiny of user contracts.

Gaetan Rassenfose: The Burden of Knowledge in the Age of Data Science. What is the future of the “inventive step concept” when we have machines which are able to make invention on their own? Gaetan presented an example: Time of Flight based imaging systems to show the different relevant knowledge sources: technical knowledge, industry knowledge (consumer products) and social knowledge (gesture steering). More and more information and knowledge is out there in the information space and has the consequence of narrowing human expertise. Research becomes more and more a search for existing knowledge and a recombination of that: The challenge is that invention must also integrate other forms of knowledge. Existing solutions are team work, lead user innovation and open innovation. Data science could be a solution to reduce costs of inventing. Therefore we have to face the upcoming discussion of patentability of computer-made inventions (complete automated inventions). This is a challenge for the future of the patent system (incentive to invent rationale).

Alissa Zeller: Influence of Digital Transformation on Freedom to Operate Processes in the Chemical Industry. How can we make sure, that we are well aligned with the compliance policy of the company when adopting a “from FTO to risk analysis approach”? Alissa gave insights from the chemical industry, freedom to operate, digitization (smart manufacturing – predictive maintenance, new business models) and described the change in the chemical business: From long termed R&D and long product cycles (one molecule, one product, empirical technology framework) towards complex inventions including IT. The Chemical industry is not driven by discrete technology anymore; it becomes cumulative and complex like the electronics, IT and computer industry. Three different FTO-approaches are in place at BASF right now: Chemical invention – classic FTO; IT invention – risk-based FTO and cross over invention – both FTO approaches combined. IP management is looking for an alignment with the BASF compliance system in terms of probability of litigation and impact on the business matrix analysis. IP risk management combines technical circumvention, stop of the project, preparing for defense to litigation, careful communication, voiding countries of high risk, indemnification of third parties and cross-licensing as risk management tools including LOT (License on Transfer) Network. BASF is looking for artificial intelligence based products for being able to improve: White space analysis, invalidity analysis, risk assessment analysis, case law analysis and infringement analyses.

Francesco Lissoni: Patent Landscaping and the Role of Information on Inventors. His key question was: How trustful is information on inventors? Inventor data is available in the digital space, but the quality of this information in patent documents is an issue. Is inventorship an attribution (or a misallocation)? Should inventorship be part of the disclosure (personal information) and how right is the information about inventors on patent documents? There are empirical signals to be in doubt whether the persons named on the patent are the true inventors. There are incentives for legitimate authors and ex post correction of inventorship. Today, no checks for inventorships are done.

Part two: About Big Data – Questions of Ownership and Use of Data

Keynote No. 2: Big Data – Ownership and Use in the Digital Age.

Reto Hilty gave an overview on the legal framework for data protection and his recommendation was a (regulated) self-regulation as a policy target. The data issue itself is not new – but the volume and nature in combination with ubiquitous connectivity and computer technology determines a new quality. Different metaphors are used by authors: data as oil, oxygen, catalyst or infrastructure (not only from private but also from public authorities). The world becomes smarter. The EU commission tries to keep pace with the commercialization of data. There are different fields of regulation: ownership of data, access to data (incl. the use of data). There are similarities to the 100 year old discussion whether we need ownership to inventions. When thinking about ownership of data (property and possession), the concrete context matters. Three kinds of data should be distinguished: data of technical/factual nature (machine data, meteorological data, market stock exchange data), personal data (health data, consumer behavior, preferences on internet/social networks, movement data from cell phones or apps) and in-between those “person attributable” data. Google maps with traffic information could be seen as an example for this kind of data. The use of data, in terms of collection, processing and function, can be regulated depending on the relevant category – and accordingly different conditions for the ownership of data can be attributed. Access to data: value lies for example in traffic data, where “attributable” data is used. It is important to distinguish the concepts of legal ownership and factual exclusivity. Reto discussed the General Data Protection Regulation from the European Union and put forward the question whether we rather need regulation or deregulation.

Timo Minssen: The Interface between Big Data, IP and Competition in the Health and Life Sciences. He explained the overlapping issues of big data, competition law and IPR. His recommendation is a recalibration of IP regulation within six overlapping IPR & related “sui generis” rights, which are relevant for big data (patents, copyright, trademarks, data base protection, trade secrets regulatory exclusivities) and competition law and IPR. He sees overlapping problems at the interface of big data & IPRs in health & life science: 1. IPRs, standardization & competition law 2. Large research infrastructures 3. Public involvement & IPRs 4. IPRs, data transparency & sharing initiatives 5. R&D incentives in precision medicine.

Aida Dolotbaeva: Big Data Analystics in the Patent World. Big data approaches within the prosecution process are searched to cope with the massive global growth of patent filings. Big Data has an impact on the patent system, especially due to the concept of novelty (absolute novelty), based on the concept of prior art. The number of patent filings grows every year on a global basis. Problem is the back log within the institutions. The typical examination time is worldwide around 8 years. Japan tries to use artificial intelligence technologies to search for prior art. Automated search attempts are used by different patent offices. Big data processing capabilities are a scenario for the transformation of the patent system. Using big data is on its way in the direction of the patent system.

Cedric Manara: Copyright and Big Data – a View from the Industry. He explained that machine learning can be affected by copyrights and his perspective is that big data is a new layer upon the copyright law. Deep learning is used for training machines to perform a specific task. Processing large sets of data is useful, getting affordable and risky – unbeknownst to many – if the data is protected by copyrights. There is a topology of constraints, which has its origins in existing copyright regulation and this introduces difficulties in administration and research: existing EU copyright laws restrict machine learning by treating data processing as copyright-relevant acts. Indirectly this issue encourages biased research, based on low-risk sources and prevent the full publication of results and thus the improvement of research. Especially in the field of artificial intelligence this is an important issue. The upcoming directive on copyright in the digital single market (proposal) gives Google which is using data within the concept of “non expressive use” an issue to think about the current politics. Big data is a new layer upon the copyright law, big data is outside of the copyright law box.

Claudia Jamin: Digitization – the speedy Change of ABB and how IP Management is affected. She described the industry 4.0 transformation of ABB and asked the question who should be the typical IP manager of the future. Industry 4.0 provokes a revolution inside of ABB. The internet of things is related to things (products), robots, motors, switch gears and controllers. ABB and Microsoft cooperate to drive the digital industrial transformation. The goal is to enhance the competence for a traditional industry with an intelligent cloud. ABB and IBM are partnering for industrial artificial intelligence solutions. 70 Mio. connected devices, 70.000 digital control systems and 6.000 enterprise software solutions are used as a source for machine data. Data is already a part of the business model of ABB. This is leading to a challenge for IP management. Which approach should be used: lean capital data industry vs. heavy capital hardware industry? There is an ongoing change of the competitive environment (cooperation partners of today, competitors of tomorrow). The challenge concerns the ownership of (big) data, and how we can protect ABB within contract regulations instead of legal protection. When we look at negotiation of ownership, exclusivity, licenses etc. joint vs. contract development, who owns results of learning of tools and apps which are owned by cooperation partners, data use of competing customers from learning apps needing customer data? One important aspect in such situations is effective conflict handling: Who is entitled, who has which standing and who is the other side?

Emmanuel de Cuenca: Big Data: IP Challenges and Opportunities, a View from Industry. He gave insights to Air Liquide, their digitally enhanced business models and the bunch of very practical IP-related business challenges. The flow of data is increasing in the gas business. Air Liquide already developed and delivered digital products. Industrial & medical assets with data are returnable assets, remote assets and production assets for optimizing costs – to improve plant performance. Big data means the 3V: Volume, Variety and Velocity. Use of big data is in the field of description (business intelligence), prediction (predictive maintenance), prescription (operation planification) and cognition (decision making). Data aggregation & analysis brings deep insights on Air Liquide business but also insights for competitors. The main issues are the need for contract protection, the patentability of software solutions, the need to sort inventions vs. mere “mathematical optimization” business methods and applications, the man to machine interface (patentability remains low) and the use of open source software codes (free software license compliance issues).

Keynote No. 3: IPM and Open Innovation – Convergence and Divergence in IoT

There is a chicken-egg-interdependency of business- and IP strategy. This explains, why IP-strategy discussions without a deep understanding of business makes no sense.

Chicken – egg: business-strategy and IP strategy. Bo presented a picture of IP as entrepreneurship – as a tool for business success. IP comes from blocking and leads to a building block of business models. He explains the ongoing change in direction coming from a primary product focus going more and more to a technology market focus. Physical products have the strong tendency to become a commodity whereas know-how and services become a critical source of competitive advantages. Technology trends force business decision makers to think about the impact of technology convergence on our current and future value proposition. Business People would like to say “patent me there” – “I want to be there”. This means that IP people have to make sure that the company can be there for its future business with exclusivity against its competitors in front of its customers. Business Model Divergence in IoT: Where are the new opportunities and threats regarding the creation of a service platform? What is IoT – a collaboration opportunity or a competitive challenge? How to design openness? Patent implications challenges: Patent eligibility, joint infringement, standard essential patents and the concept of FRAND and exhaustion of rights. Patent implication opportunities: Portfolio licensing reduces patent quality, patent pools reduce royalty stack/gap issues. Recommendations: Control to regulate openness. Here, meaningful control mechanisms could be: Technical control, market power (network effects), secrecy, right based property and contract based property. His conclusion: In the knowledge economy we are all developing countries.

Third Panel: Patentability of Technical Software Solutions and the Exploitation Model for Digital Innovation

Yann Ménière: The EPO Approach to Industry 4.0 and the Internet of Things. He presented the EPO perspective on the digital transformation and the challenge of software based inventions. How do EPO examiners see the ongoing digital transformation? Core is the IoT: Ubiquitous connected computers within the internet of everything. Things are connected to the environment and to people. This is the source of massive data. In combination with analytics we can create new services and business models. The enabling technologies are already there: It’s all about software. Today advanced machines are using standard software – 4.0 advanced software is using standard machines. Around 30% of the inventions in the different branches are software associated. Software is used as a tool in an invention. The Challenge is to deal with increasingly short innovation and product life cycles.

Catalina Martinez: Patents and Software: Changing Markets, Strategies and Actors. She gave an overview of the changing situation for patenting software over the last fourteen years. How can software be patented? Patents protect the structure of a computer program and its functionality – the flow chart. Google page rank US6285999 – Method for node ranking in a linked database – the invention is the structure. Cited around the world more than 1.000 times, alone 500 citations from Google itself. From software to Computer implemented inventions: The share of US patents that can be classified under “Electrical Engineering”, a class that includes digital communications, computer technology, and communications, has grown markedly. FTO is the most important motivation for filing patens with computer implemented inventions. Not only software companies alone file for cii patents anymore – it is now done in every business. Pervasiveness of software related patents: in nearly all sectors. Some tentative recommendations: Increasing transparency, inventive step, disclosure.

Ryan Abbott: Creative Computers and the Future of Patent Law. Can a computer be an inventor and how will computers compete on inventive thinking? Computers will potentially be working as doctors, lawyers and experts. This raises the question of the inventor for machine created invention. The degree of human involvement in invention processes is in question. Different degrees are thinkable: Computer as a tool, computer assisted invention and finally computer as creative source. Can an ape who makes a selfie be seen as a copyright owner? Who is identified when a computer can be an inventor? The developer of the computer? Incentive argumentation in the patent system could be used. Focusing on the functional outcome of inventive thinking helps and not so much on what the inventor was thinking. Computer and people will be competing on inventive thinking. Skilled person test becomes more and more difficult – Watson is a substitute of a person skilled in the art.

Stephanie van Wermeskerken: How to get to Successful Value Capturing and Exploitation of Digital IP? How can a digital patent portfolio be transformed into a real value for a company? How can real value be squeezed from a digital patent portfolio? Philips focuses on health care and personal care. Capturing value higher than the IP cost is the only justification for having an IP portfolio. IP&S uses an IIAM approach to create and leverage synergetic effects from multiple IP types (patents, deigns, trade secrets, algorithms, brands, online presence) – to make sure to protect the “look and feel”. Capturing value with an IP portfolio – commercial/Licensing, exclusivity, mitigation, defensive – creating business benefits. Using IP to create market entry barriers against potential competitors. Distinguishing wide (longitudinal, continuous monitoring over time) data from deep data (more detailed information than ever) – dense data (big data pattern recognition). Some considerations for a mitigation model – how does this bring benefit? Starting point – there is a balance in IP position in terms of strength, number, and geographical coverage. It is difficult to operate without risk of IP assertion – benefit risk mitigation – exchange – gaining access to 3rd party technology, exchange – saving time and money.

Closing: Alexander Wurzer, Adjunct Professor at CEIPI, Director of Studies of the Master in IP Law and Management of CEIPI, Director of Institute for Intellectual Property Management at Steinbeis-University, Berlin, Managing Director of Wurzer & Kollegen.

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